Theme Issue on AI for Smart Applications最新文献

Multiple structural defect detection for reinforced concrete buildings using YOLOv5s 基于YOLOv5s的钢筋混凝土建筑多重结构缺陷检测

Theme Issue on AI for Smart Applications Pub Date : 2022-06-30 DOI: 10.33430/v29n2thie-2021-0033

Chaobin Li, Wei Pan, P. Yuen, R. Su

{"title":"Multiple structural defect detection for reinforced concrete buildings using YOLOv5s","authors":"Chaobin Li, Wei Pan, P. Yuen, R. Su","doi":"10.33430/v29n2thie-2021-0033","DOIUrl":"https://doi.org/10.33430/v29n2thie-2021-0033","url":null,"abstract":"Building inspection and maintenance are becoming increasingly essential means by which to consider the deterioration problems of old, reinforced concrete (RC) buildings. While such inspection work can be conducted with the aid of computer vision-based technology, this technology remains challenged, since real-world structural defects and environmental conditions are varied and complex. In recent years, object detection algorithms have improved to achieve greater speed and accuracy with the help of deep learning. In this paper, an advanced object detector, YOLOv5s, was successfully applied to the recognition of common structural defects including cracks, delamination, exposed reinforcement, rust stains, spalling, tile cracks, tile delamination, and tile loss. Compared with the other advanced object detectors of YOLO (i.e., YOLOv5m, YOLOv5l, YOLOv5x, YOLOv4, and YOLOv3) based on the built data set, the YOLOv5s algorithm shows an obvious advantage for defect detection, achieving 64.5% and 67.0% mean average precision (mAP) for training and testing, respectively. It also takes less than 0.1 seconds to detect a defect on an image. The lightweight and high detection performance of the YOLOv5s algorithm shows great promise for potential deployment on an onboard inspection device, such as an unmanned aerial vehicle (UAV) or a robot, to achieve real-time structural inspection.","PeriodicalId":284201,"journal":{"name":"Theme Issue on AI for Smart Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128268380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective real-time face mask detection on NVIDIA edge devices 在NVIDIA边缘设备上有效的实时面罩检测

Theme Issue on AI for Smart Applications Pub Date : 2022-06-30 DOI: 10.33430/v29n2thie-2021-0029

Ho Chuen Kam, Nathan Chung Kan Lui, Ethan Yi Chen Du, Marcus Ngai Kan Lui, Chun Sing Chan, Yan Ting Cheung, K. Cheung, Monica Sze Man Leung

引用次数: 0

Application of artificial intelligence (AI) control system on chiller plant at MTR station 人工智能(AI)控制系统在地铁站冷水机组上的应用

Theme Issue on AI for Smart Applications Pub Date : 2022-06-30 DOI: 10.33430/v29n2thie-2021-0032

Alison Tsz Yan Suen, David Tik Wai Ying, Chris Choy

{"title":"Application of artificial intelligence (AI) control system on chiller plant at MTR station","authors":"Alison Tsz Yan Suen, David Tik Wai Ying, Chris Choy","doi":"10.33430/v29n2thie-2021-0032","DOIUrl":"https://doi.org/10.33430/v29n2thie-2021-0032","url":null,"abstract":"Chillers account for up to 40% of total station energy consumption in the Hong Kong Mass Transit Railway (MTR) system. As part of green railway initiatives, a site trial was conducted to apply a fully automated AI system to control a chiller plant in order to optimise energy performance in real time while maintaining a level of passenger comfort that suits each station’s environment. Through the predictive power of the AI system, the plant power’s consumption and cooling demands can be forecasted based on actual chiller, station, and weather conditions, all of which vary over time. The optimal operational settings can then be determined using an optimisation model for real-time chiller plant control, including staging, sequencing, chilled water supply temperature set-point, etc. This paper presents the formulation of an AI system using data-driven machine learning models and numerical optimisation, and the comparison of the actual energy performance of the proposed system against rule-based control optimisation in a conventional building management system (BMS) through the site trial. The results revealed the proposed AI system achieves better energy efficiency with annual energy savings of approximately 8.7%.","PeriodicalId":284201,"journal":{"name":"Theme Issue on AI for Smart Applications","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116295715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence applications for proactive environmental monitoring and asset management 用于主动环境监测和资产管理的人工智能应用

Theme Issue on AI for Smart Applications Pub Date : 2022-06-30 DOI: 10.33430/v29n2thie-2021-0028

J. Chow, P. S. Tan, Kuan-fu Liu, Xin Mao, Zhaoyu Su, Ghee Leng Ooi, Yehur Cheong, M. Leung, Jimmy Wu, Hok Man Chan, L. Y. Yip, Ka Chun Chow, Yu-Hsing Wang

{"title":"Artificial intelligence applications for proactive environmental monitoring and asset management","authors":"J. Chow, P. S. Tan, Kuan-fu Liu, Xin Mao, Zhaoyu Su, Ghee Leng Ooi, Yehur Cheong, M. Leung, Jimmy Wu, Hok Man Chan, L. Y. Yip, Ka Chun Chow, Yu-Hsing Wang","doi":"10.33430/v29n2thie-2021-0028","DOIUrl":"https://doi.org/10.33430/v29n2thie-2021-0028","url":null,"abstract":"Two research studies have been implemented to explore the potential of applying artificial intelligence (AI) technologies in works projects and maintenance work of the Drainage Services Department (DSD) for enhancing the efficiency related to environmental monitoring and structural inspection, referred to as the AIEIA and AIBIM projects, respectively. In the AIEIA project, AI technologies were explored to assist observing bird behaviour that would likely be influenced by nearby DSD construction projects. A domain randomisation-enhanced model was built to detect great egrets and little egrets at Penfold Park, Hong Kong, achieving a mean average precision of 87.65%. The detection result was used to analyse the Penfold Park egretry behaviour. In the AIBIM project, AI technologies were used to facilitate the condition assessment of concrete defects in sewage treatment facilities. A classifier was developed with supervised learning for concrete defect detection, attaining recalls of 86.2% and 89.9% for the cracking and spalling classes. Another concrete defect anomaly detector was built using unsupervised learning, achieving balanced results with F2 measures of 85.2% and 76.0% for the cracking and spalling classes. The two research studies render valuable experience for the DSD to integrate AI-enabled analytics into future work to continuously improve the drainage services in Hong Kong.","PeriodicalId":284201,"journal":{"name":"Theme Issue on AI for Smart Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130473099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HRSG early tube leak detection with a transfer learning neural network and Gramian Angular Difference Field 基于迁移学习神经网络和Gramian角差场的HRSG早期管道泄漏检测

Theme Issue on AI for Smart Applications Pub Date : 2022-06-30 DOI: 10.33430/v29n2thie-2021-0027

H. F. Chow

引用次数: 0

Development of an AI model for electronic board maintenance decision prediction for railway equipment 铁路设备电子板维修决策预测人工智能模型的开发

Theme Issue on AI for Smart Applications Pub Date : 2022-06-30 DOI: 10.33430/v29n2thie-2021-0025

Ken Yat Hung Li, John See Jing Leung, Laura Ming Wai Lau

{"title":"Development of an AI model for electronic board maintenance decision prediction for railway equipment","authors":"Ken Yat Hung Li, John See Jing Leung, Laura Ming Wai Lau","doi":"10.33430/v29n2thie-2021-0025","DOIUrl":"https://doi.org/10.33430/v29n2thie-2021-0025","url":null,"abstract":"Railway equipment is required to work fault-free under rugged conditions such as continuous operating heat loads. One of the most important considerations in railway maintenance is the ability to predict failure, due to aging, early enough such that spare parts can be acquired just-in-time which normally takes a lead time of several weeks to up to around a year from the supplier. This study has conceived and tested the RUS Boost Ensemble machine learning algorithm to predict maintenance decisions of railway electronic boards based on measurable component values. Some traditional approaches like MIL-217 are commonly used for electronic reliability prediction but these types of approaches do not consider load profiles, failure root causes, and practical limitations in the number of available experimental test samples. This study develops a prognostic approach by considering actual load conditions and life limiting factors, and utilises machine learning algorithms to build the model. The model also makes use of real-life test samples from lab ALT (Accelerated Life Testing). After development of the AI model to predict electronic board component maintenance, the test results revealed the predictive accuracy to have up to 95% correlation for the red/urgent category and 94% for the yellow/medium-urgency category.","PeriodicalId":284201,"journal":{"name":"Theme Issue on AI for Smart Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125651488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0